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35 results for “Amundsen Sea”
Ensemble of NEMO present-day (1989-2009) and future (2080-2100 under RCP8.5) ocean properties and ice shelf melt rates in the Amundsen Sea
<p>Model outputs used in <a href="https://www.essoar.org/doi/10.1002/essoar.10511482.3">Jourdain et al. (GRL, 2022)</a></p> <p>The output files consist of monthly climatologies over either 1989-2009 or 2080-2100. The file names have the form:</p> <p><strong>climato_monthly_AMUXL12-GNJ002_<simu>_<group>_1989_2009.nc</strong>, where :</p> <ul> <li><simu> is either : <ul> <li>"BM02MAR" (ensemble member A, present-day),</li> <li>"BM03MAR" (ensemble member B, present-day),</li> <li>"BM04MAR" (ensemble member C, present-day),</li> <li>"BM02MARrcp85" (ensemble member A, future for both surface and lateral boundaries),</li> <li>"BM03MARrcp85" (ensemble member B, future for surface BUT NOT for lateral boundaries),</li> <li>"BM03MARrcBDY" (ensemble member B, future for both surface and lateral boundaries),</li> <li>"BM04MARrcp85" (ensemble member C, future for both surface and lateral boundaries),</li> </ul> </li> <li><group> is either : <ul> <li>"SBC" (surface boundary conditions),</li> <li>"icemod" (sea ice variables),</li> <li>"gridT" (temperature, salinity),</li> <li>"gridU" (zonal velocities),</li> <li>"gridV" (meridional velocities).</li> </ul> </li> </ul> <p>Grid information in:</p> <ul> <li>mesh_mask_AMUXL12_BedMachineAntarctica-2019-05-24.nc (ensemble member A),</li> <li>mesh_mask_AMUXL12_BedMachineAntarctica-2020-07-15_v02_ICB380.nc (ensemble members B & C).</li> </ul> <p>where:</p> <ul> <li>glamt : longitude</li> <li>gphit: latitude</li> <li>e1t, e2t, e3t_0 : mesh size (in meters) along x, y, z</li> <li>tmask = 1 for ocean mesh, = 0 otherwise (land, continental ice).</li> </ul> <p> </p> <p><strong>Acknowledgments:</strong> This work was granted access to the HPC resources of CINES (occigen) under the allocation A0100106035 attributed by GENCI.</p>
Amundsen Sea MAR simulations forced by ERAinterim
<p><strong>MAR simulations produced by Marion Donat-Magnin at IGE, Grenoble, France.</strong></p> <p><br> This simulation is evaluated in the following article:</p> <p>Donat-Magnin, M., Jourdain, N. C., Gallée, H., Amory, C., Kittel, C., Fettweis, X., Wille, J. D., Favier, V., Drira, A., and Agosta, C. (2020). Interannual variability of summer surface mass balance and surface melting in the Amundsen sector, West Antarctica, The Cryosphere, 14, 229–249, <a href="https://doi.org/10.5194/tc-14-229-2020">https://doi.org/10.5194/tc-14-229-2020</a> </p> <p><br> Here are provided the monthly means over 1979-2017. Daily outputs available on demand.<br> <br> See netcdf metadata for more information. Note that what is called runoff in the outputs is not actually a runoff (into the ocean) but more the net production of liquid water at the surface (which can either form ponds or flow into the ocean).</p> <p> </p> <p>Monthly files provided on MAR grid (see MAR_grid10km.nc). We also provide climatological (1979-2017 average) surface mass balance (SMB), surface melt rates and net liquid water production ("runoff") on a standard 8km WGS84 stereographic grid (see files ending as mean_polar_stereo.nc).</p> <p> </p> <p>The following variables are provided:</p> <ul> <li>CC Cloud Cover</li> <li>LHF Latent Heat Flux</li> <li>LWD Long Wave Downward</li> <li>LWU Long Wave Upward</li> <li>QQp Specific Humidity (pressure levels)</li> <li>QQz Specific Humidity (height levels)</li> <li>RH Relative Humidity</li> <li>SHF Sensible Heat Flux</li> <li>SIC Sea ice cover</li> <li>SP Surface Pressure</li> <li>ST Surface Temperature</li> <li>SWD Short Wave Downward</li> <li>SWU Short Wave Upward</li> <li>TI1 Ice/Snow Temperature (snow-layer levels)</li> <li>TTz Temperature (height levels)</li> <li>UUp x-Wind Speed component (pressure levels)</li> <li>UUz x-Wind Speed component (height levels)</li> <li>VVp y-Wind Speed component (pressure levels)</li> <li>VVz y-Wind Speed component (height levels)</li> <li>UVp Horizontal Wind Speed (pressure levels)</li> <li>UVz Horizontal Wind Speed (height levels)</li> <li>ZZp Geopotential Height (pressure levels)</li> <li>mlt Surface melt rate</li> <li>rfz Refreezing rate</li> <li>rnf Rainfall</li> <li>rof Runoff (i.e. net production of surface liquid water)</li> <li>sbl Sublimation</li> <li>smb Surface Mass Balance</li> <li>snf Snowfall</li> </ul>
Sensitivity maps of the Amundsen Sea Embayment to changes in external forcings using Automatic Differentiation
<p>Sensitivity maps of the final volume above flotation after 20 years to the basal friction coefficient, rheology factor, surface mass balance, and ocean-induced melting. These results were computed from STREAMICE and ISSM using automatic differentiation. See manuscript for complete description</p>
Data used in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)"
<p>Data files used in the analysis in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)".</p> <p>Data were collected during the RV NB Palmer NBP2202 cruise, during the 2022 TARSAN campagine in the Amundsen Sea.</p> <p>Underway data provides daily files from the underway and meteorology sensors in JGOFS format. CTD data collected from the cruise. Information about sensors and data formats is included in the data report.</p> <p>Glider data was processed through the UEA Seaglider Toolbox (https://bitbucket.org/bastienqueste/uea-seaglider-toolbox/src/toolbox/) and is provided in Matlab format.</p> <p> </p> <p>Manuscript abstract:</p> <p>In coastal polynyas, where sea–ice formation occurs, it is crucial to have accurate estimates of heat fluxes in order to predict future rates of sea–ice formation. The Amundsen Sea Polynya is the fourth largest coastal polynya around Antarctica, yet remains poorly observed because of its remoteness. Consequently, we rely on models and reanalysis that are unvalidated to study the effect of atmospheric forcing on polynya dynamics. We use summer ship-board data from the NBP22/02 cruise to understand the turbulent heat flux dynamics in the Amundsen Sea Polynya and evaluate our ability to represent these dynamics in ERA5. We show that cold and dry air outbreaks from Antarctica enhance air–sea temperature and humidity gradients, triggering episodic heat loss events. The heat loss is larger along the ice shelves, and it is also where the ERA5 turbulent heat flux exhibits the largest biases, underestimating the flux by up to 141~W~m$^{-2}$ due to its coarse resolution and misrepresentation of ice-shelf location. By reconstructing a turbulent heat flux product from ERA5 variables using a nearest neighbour approach to obtain sea surface temperature, we decrease the bias to 107 W m$^{-2}$. Using a 1D-model, we show that the mean co-located ERA5 heat loss underestimation of -28~W~m$^{-2}$ led to an overestimation of the summer evolution of sea surface temperature (heat content) by +0.76~°C (+8.2e+07~J) over 35-days. By obtaining the reconstructed flux, the reduced heat loss bias (12 W~m$^{-2}$) reduced the seasonal bias in sea surface temperature (heat content) to -0.17~°C (-3.30e+07~J) over the 35-days. This study shows that caution should be applied when retrieving ERA5 turbulent flux along the ice shelves, and that a reconstructed flux using ERA5 variables shows better accuracy.</p> <p> </p> <p> </p>
BISICLES ice-sheet model for the Amundsen Sea Embayment, Antarctica : ensemble simulations to 2050
<p>BISICLES ice-sheet model simulations for the Amundsen Sea Embayment. Full details of the model set-up and ensemble design are described in the attached manuscript which has been accepted for publication in Journal of Glaciology.<br> In brief, a 213-member ensemble of simulations was created by varying four different model parameters. The parameters are the u0 value in a regularised Coulomb friction law, the rate of imposed thinning of floating ice (∂h/∂t(Ωf)), and scaling factors for sliding and viscosity coefficients (<em>C</em> and ϕ) between 0.9 and 1.1. We attach a summary text file of results, as well as NetCDF files of simulated variables land ice thickness and u and v components of velocity.<br> <strong>ASE2050_bisicles.csv </strong>contains annual (2007 to 2050, columns 5 to 48) sea level equivalent (mm) mass losses of ice from the Pine Island and Thwaites Glacier catchment basins. The parameters, given in columns 1 to 4, respectively, are the u0 (m/a), the rate of imposed thinning of floating ice (m/a), and the scaling factors for sliding and viscosity coefficients.<br> The NetCDF files in <strong>ASE_BISICLES.tar.gz</strong> contain annual (2007 to 2050) simulated output variables for the Amundsen Sea region at a spatial resolution of 1 km, with one file per ensemble member. The variables follow the ISMIP6 naming protocol:<br> (https://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica#A2.3_Model_output_variables_and_README_file).<br> We include state variables lithk, uvelmean, and vvelmean. Each file is named according to the variable, the simulation parameters, and the resultant 2050 SLE value of ice loss (mm). For example, <strong>lithk_ASE_BISICLES.uj_20.dhfdt_5.C_0.90.phi_0.90.slr_43.06.nc </strong>is the land ice thickness data for simulation u0=20 m/a, ∂h/∂t(Ωf) = 5 m/a, C scaled by 0.9, ϕ scaled by 0.9, and a final SLE of 43.06 mm.</p> <p> </p>
PROTECT-SLR BISICLES ice-sheet model simulations for Amundsen Sea Embayment to 2050
<p>BISICLES ice-sheet model results. The NetCDF files in ASE_BISICLES.tar.gz contain simulated output variables for the Amundsen Sea Embayment sector of the West Antarctic Ice Sheet at a spatial resolution of 1 km. The model start date is 2007 and the outputs are yearly to 2052. Each of the 30 simulations is a result of a different combination of model parameters. The parameters are the u<sub>0</sub> value in a regularized Coulomb friction law, the rate of imposed thinning of floating ice, and scaling factors for sliding and viscosity coefficients between 0.9 and 1.1. The final part of each dataset name gives the sea-level equivalent (SLE) of loss of ice above floatation within Pine Island and Thwaites Glacier catchment basins. Each output was randomly selected from a 2 cm 2050 SLE band of a histogram of a large ensemble of simulations.</p> <p>See the pdf report included for further details.</p>
Figure 5 in Acutiserolis poorei sp. nov. from the Amundsen and Bellingshausen Seas, Southern Ocean (Crustacea, Isopoda, Serolidae)
Figure 5. Photograph of Acutiserolis spinosa (Kussakin, 1967) (Zoological Museum of St. Petersburg) # 46416, holotype male of 32 mm length.
Figure 2 in Acutiserolis poorei sp. nov. from the Amundsen and Bellingshausen Seas, Southern Ocean (Crustacea, Isopoda, Serolidae)
Figure 2 Acutiserolis poorei sp. nov., paratype female, head ventrally (A); paratype male, incisor of left and right mandible and mandibular palp, maxillula and maxilla; paratype female, antennula, antenna, pereopod 2 and pleopod 2.
Figure 1 in Acutiserolis poorei sp. nov. from the Amundsen and Bellingshausen Seas, Southern Ocean (Crustacea, Isopoda, Serolidae)
Figure 1. Acutiserolis poorei sp. nov., holotype female in dorsal (A) and lateral (B) view, pleotelson of paratype male (C) and ventral part of paratype male (D).
Surface elevation change of the Amundsen Sea Embayment 1992-2019
<p>This dataset consists of grids of dh/dt computed over 3-year periods as well as annual grids of average dh and their corresponding uncertainties covering the period 1992 to 2019 using satellite radar altimetry data from ERS-1/2, ENVISAT and CryoSat-2 over the Amundsen Sea Embayment. This dataset has been prepared for the publication 'Amundsen Sea Embayment ice-sheet mass-loss predictions to 2050 calibrated using observations of velocity and elevation change' by Bevan et al. (2023, accepted), <em>Journal of Glaciology.</em></p> <p>The methods used for the derivation of this dataset are described in Shepherd et al, Trends in Antarctic ice sheet elevation and mass, 2019, GRL vol 46 issue 14 pp 8174-8183, <a href="https://doi.org/10.1029/2019GL082182">https://doi.org/10.1029/2019GL082182</a>. </p>
Climatic-oceanographic controls on Amundsen Sea productivity and bottom flow evolution since 280 ka
<p>Understanding the mechanisms regulating productivity and bottom flow variability in the Amundsen Sea is vital for constraining cryosphere-climate feedbacks in the South Pacific Seasonal Sea Ice Zone. To enhance our comprehension of the mechanisms underlying productivity variations in this region, we present an extensive set of multi-proxy records from gravity core ANT36/A3-08 (LATITUDE: -69.02 and LONGITUDE: -120.06), retrieved from the southernmost upwelling boundary of the modern Circumpolar Deep Water in the Amundsen Sea.</p> <p>Surface productivity is reconstructed using a suite of biogeochemical proxies, including Ti-normalized bromine, biogenic opal, total nitrogen, and excess barium normalized to Ti. These proxies reflect marine biogenic export production and nutrient cycling. Bottom flow strength is inferred from sedimentological and geochemical indicators such as mean sortable silt, sortable silt percentage, zirconium-to-rubidium, and zirconium-to-aluminum ratios.</p>
Chlorophyll production in the Amundsen Sea boosts heat flux to atmosphere and weakens heat flux to ice shelves -> Model outputs
<p>This repository contains MITgcm outputs associated with the paper "Chlorophyll production in the Amundsen Sea boosts heat flux to atmosphere and weakens heat flux to ice shelves", submitted to the Journal of Geophysical Research: Oceans. The outputs are presented in netCDF format and come from two simulations - <em><strong>GREEN</strong></em>, with chlorophyll, and <em><strong>BLUE</strong></em>, without chlorophyll affecting shortwave heating.</p> <ul> <li>Temperature and salinity <ul> <li>green_temp.nc <ul> <li>3-dimensional monthly fields of temperature in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C</li> </ul> </li> <li>green_salt.nc <ul> <li>3-dimensional monthly fields of salinity in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: g/kg</li> </ul> </li> <li>green_ohc.nc <ul> <li>3-dimensional monthly fields of ocean heat content trend in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C/day</li> </ul> </li> <li>blue_temp.nc <ul> <li>3-dimensional monthly fields of temperature in the <em><strong>BLUE</strong><strong> </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C</li> </ul> </li> <li>blue_salt.nc <ul> <li>3-dimensional monthly fields of salinity in the <em><strong>BLUE </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: g/kg</li> </ul> </li> <li>blue_ohc.nc <ul> <li>3-dimensional monthly fields of ocean heat content trend in the <em><strong>BLUE </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C/day</li> </ul> </li> </ul> </li> <li>Sea ice <ul> <li>ice_green.nc <ul> <li>2-dimensional monthly fields of sea ice concentration in the <em><strong>GREEN</strong></em><em><strong> </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>range: 0 -> 1</li> </ul> </li> <li>sit_green.nc <ul> <li>2-dimensional monthly fields of sea ice effective thickness in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: m</li> </ul> </li> <li>ice_blue.nc <ul> <li>2-dimensional monthly fields of sea ice concentration in the <em><strong>BLUE </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>range: 0 -> 1</li> </ul> </li> <li> sit_blue.nc <ul> <li>2-dimensional monthly fields of sea ice effective thickness in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: m</li> </ul> </li> </ul> </li> <li>Ice shelves <ul> <li>green_meltrate.nc <ul> <li>2-dimensional monthly fields of ice shelf basal melt in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: kg/m^2/s</li> </ul> </li> <li>blue_meltrate.nc <ul> <li>2-dimensional monthly fields of ice shelf basal melt in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: kg/m^2/s</li> </ul> </li> </ul> </li> <li>Surface heat fluxes <ul> <li>tflux_green.nc <ul> <li>2-dimensional monthly fields of total downward surface heat flux in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>latent_green.nc <ul> <li>2-dimensional monthly fields of downward latent heat flux in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>sensible_green.nc <ul> <li>2-dimensional monthly fields of downward sensible heat flux in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>longwave_green.nc <ul> <li>2-dimensional monthly fields of upward longwave heat flux in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>shortwave_green.nc <ul> <li>2-dimensional monthly fields of upward shortwave heat flux in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>tflux_blue.nc <ul> <li>2-dimensional monthly fields of total downward surface heat flux in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>latent_blue.nc <ul> <li>2-dimensional monthly fields of downward latent heat flux in the <em><strong>BLUE</strong></em><em><strong> </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>sensible_blue.nc <ul> <li>2-dimensional monthly fields of downward sensible heat flux in the <em><strong>BLUE </strong></em>experiment </li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>longwave_blue.nc <ul> <li>2-dimensional monthly fields of upward longwave heat flux in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>shortwave_blue.nc <ul> <li>2-dimensional monthly fields of upward shortwave heat flux in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> </ul> </li> <li>BLING <ul> <li>chlorophyll.nc <ul> <li>2-dimensional monthly fields of surface chlorophyll in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2003 -> 31.12.2014</li> <li>units: mg/m^3</li> </ul> </li> <li>euphotic_depth.nc <ul> <li>2-dimensional monthly fields of euphotic depth in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2003 -> 31.12.2014</li> <li>units: m</li> </ul> </li> </ul> </li> </ul>
Figure 4 in Acutiserolis poorei sp. nov. from the Amundsen and Bellingshausen Seas, Southern Ocean (Crustacea, Isopoda, Serolidae)
Figure 4 Acutiserolis poorei sp. nov., paratype male, pereopod 6, pleopods 1–5, uropod.
Figure 3 in Acutiserolis poorei sp. nov. from the Amundsen and Bellingshausen Seas, Southern Ocean (Crustacea, Isopoda, Serolidae)
Figure 3 Acutiserolis poorei sp. nov., paratype male, pereopods 1–5, pereopod 7.
Reconstruction from Multi-Decadal Variability of Amundsen Sea Low Controlled by Natural Tropical and Anthropogenic Drivers
<p>This archive contains the sea-level pressure, sea surface temperature and sea ice concentration fields from the <em>unforced</em> reconstruction in the Southern Hemisphere and tropical regions over 1871–2000 CE presented in the following study:</p> <p>Dalaiden, Q., Abram, N.J., Goosse, H., Holland, P., O’Connor G.K. and Topál, D., Multi-Decadal Variability of Amundsen Sea Low Controlled by Natural Tropical and Anthropogenic Drivers, Geophysical Research Letters, https://doi.org/10.1029/2024GL109137 (2024).</p> <p>In addition to the <em>unforced </em>reconstruction, the total variability (i.e., unforced and forced variability) of the mean sea-level pressure over the Amundsen Sea Low area is also provided. </p> <p>Each NetCDF file corresponds to one variable. All fields are anomalies relative to the 1961–1990 CE period. The variables included in the archive are:</p> <p>SLP: Sea-level pressure</p> <p>mean_ASL: mean sea-level pressure over the Amundsen Sea Low area</p> <p>IPO: Interdecadal Pacific Oscillation Index</p> <p>SST: Sea surface temperature</p> <p>SIC: Sea ice concentration</p> <p>An estimation of the uncertainty is provided for each variable, except for the total variability of the mean sea-level pressure over the Amundsen Sea Low area.</p> <p>Please contact Quentin Dalaiden (quentin.dalaiden@uclouvain.be; quentin.dalaiden@gmail.com) for more information.</p>
Data for The influence of bathymetry over heat transport onto the Amundsen Sea continental shelf
<p>Data and code for producing figures for the paper 'The influence of bathymetry over heat transport onto the Amundsen Sea continental shelf'</p>
Spatially and temporally continuous reconstruction of Antarctic Amundsen Sea sector ice sheet surface velocities: 1996-2018
<p>Spatially and temporally continuous reconstruction of ice sheet surface velocities for the Amundson Sea Sector of the Antarctica. The reconstruction is derived from the synthesis of annual published InSAR (R14: Rignot et al. 2014) and optical (G18: Gardner et al., 2018 & Gardner et al., 2022) surface velocities. Data are posted on a uniform 240 m by 240 m grid in Antarctic Polar Stereographic (EPSG:3031) coordinates. The temporal posting is every 2.4 months or 1/5 of a year.</p> <p>R14 and G18 annual velocity data have large errors and data gaps in both space and time that make the data challenging to work with. For this reason, a Spatially and temporally continuous reconstruction was made. These are the preprocessing steps that were applied to create the reconstruction:</p> <ol> <li>R14 component velocities [vx/vy] are mapped to the same 240-m grid as G18 for the Amundson Sea sector.</li> <li>Velocities falling outside of mapped ice extents (see Paolo et al., 2022) are set to no data values.</li> <li>A reference velocity is defined as the 1996 velocity field or the earliest valid measurement thereafter. The average of both velocities is taken if multiple observations exist for the first year of data.</li> <li>For areas moving faster than 200 m/yr., the percentage anomalies are calculated for all years relative the reference velocity. This was done for both G18 and R14 velocities separately.</li> <li>Annual velocity anomalies are then filter with a 5-km windowed moving median.</li> <li>G18 and R14 filtered anomalies are merge by taking the mean of each year. Years with less than 30% coverage for fast moving ice (>= 200 m/yr.) were discarded.</li> <li>If missing annual values were within 25 km of a valid datapoint they are filled using natural neighbor interpolation, otherwise anomalies were set to zero.</li> <li>Outside of fast-moving areas, annual anomalies are tapered to zero using a 10-km cosine taper.</li> <li>Merged and filled annual anomalies are then smoothed one last time using a 5-km windowed moving mean.</li> <li>To create a continuous record of velocity, annual anomalies are interpolated in time to every 1/5 of a year for every 240 m pixel using a spline interpolant and multiplied by the reference velocity.</li> </ol> <p>All x and y component velocities [vx/vy] and velocity magnitudes [v] are stored as individual geotiff files and are contained in the .zip included file. A visualization of the velocity magnitudes is included as an animated gif. </p> <p> </p> <p>References:</p> <p>Gardner, A., M. Fahnestock, and T. Scambos. (2022). MEaSUREs ITS_LIVE Regional Glacier and Ice Sheet Surface Velocities, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/6II6VW8LLWJ7. Date Accessed 04-07-2019.<br> <br> Gardner, A. S., Moholdt, G., Scambos, T., Fahnstock, M., Ligtenberg, S., van den Broeke, M., & Nilsson, J. (2018). Increased West Antarctic and unchanged East Antarctic ice discharge over the last 7 years. <em>The Cryosphere</em>, <em>12</em>(2), 521–547. https://doi.org/10.5194/tc-12-521-2018</p> <p>Paolo, F., Gardner, A., Greene, C., Nilsson, J., Schodlok, M., Schlegel, N., & Fricker, H. (2022). Widespread slowdown in thinning rates of West Antarctic Ice Shelves. <em>EGUsphere</em>, <em>2022</em>, 1–45. https://doi.org/10.5194/egusphere-2022-1128</p> <p>Rignot, E., J. Mouginot, and B. Scheuchl. (2014). MEaSUREs InSAR-Based Ice Velocity of the Amundsen Sea Embayment, Antarctica, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0545.001. Date Accessed 04-07-2019.</p>
Exploring the roles of iron and irradiance in dynamics of diatoms and Phaeocystis in the Amundsen Sea continental shelf water
<p>The Amundsen Sea continental shelf (ACS) water ecosystem is expected to undergo changes since the increasing melt rate of glaciers and decreasing sea ice extent by global warming would lead to the mitigation of iron and light limitation. We investigated how diatoms and Phaeocystis, two dominant taxa, and primary production in the ACS water would respond to variations in iron and light availabilities by using a 1-D pelagic ecosystem model. In the model, we added sea ice effects that reduce light penetration and optimized model parameters for diatoms and Phaeocystis. The results from our model showed good agreement with 20-year observations of Chl-a as well as the biomass proportion of diatoms and Phaeocystis and nutrient distributions during the growing season. Our model experimental results suggest that the current moderate iron and high light conditions favor the growth of Phaeocystis over diatoms. Moreover, as iron increases, the organic carbon exudation by phytoplankton increases more rapidly than net primary production (NPP), leading to a decline in phytoplankton biomass. On the other hand, irradiance plays a role in controlling NPP in terms of photoinhibition which is reduced by increasing iron. Increases in both iron and irradiance lead to an advance in the timing of the bloom peak (surface Chl-a maximum) due to increases in phytoplankton carbon loss and photoinhibition. Our results imply that the dominance of Phaeocystis can continue and that the carbon uptake capacity of the ACS in the summer seasons might increase given that iron availability will increase with future climate change.</p>
Amundsen Sea future MAR simulations forced by the CMIP5 multi-model mean
<p><strong>Amundsen Sea future MAR simulation forced by the CMIP5 multi-model mean (RCP8.5)</strong></p> <p>This future simulation is fully described in the following article:</p> <p>Donat-Magnin, M., Jourdain, N. C., Kittel, C., Agosta, C., Amory, C., Gallée, H., Krinner, G., and Chekki, M. Future surface mass balance and surface melt in the Amundsen sector of the West Antarctic Ice Sheet. <em>The Cryosphere</em>.</p> <p>The future is derived from the CMIP5 multi-model mean under the RCP8.5 scenario and covers the 2079-2108 period. The corresponding present-day simulation is available on <a href="http://doi.org/10.5281/zenodo.4308510">http://doi.org/10.5281/zenodo.4308510</a> and was thoroughly evaluated in the following TC paper: <a href="https://doi.org/10.5194/tc-14-229-2020">https://doi.org/10.5194/tc-14-229-2020</a></p> <p>See netcdf metadata for more information. Note that what is called runoff in the outputs is not actually a runoff (into the ocean) but more the net production of liquid water at the surface (which can either form ponds or flow into the ocean).</p> <p>Monthly files provided on MAR grid (see MAR_grid10km.nc). We also provide climatological (2079-2108 average) surface mass balance (SMB), surface melt rates and net liquid water production ("runoff") on a standard 8km WGS84 stereographic grid (see files ending as mean_polar_stereo.nc). Daily snowfall and surface melt rates are provided in ICE*nc.<br> <br> The interpolation to the stereographic grid is done using interpolate_to_std_polar_stereographic.f90. The fields are extrapolated to the ocean grid points so that ice sheet models with various ice-shelf extent can use this dataset.</p> <p>To extrapolate the SMB and surface melt projections to other warming scenarios or period, see eq. (2,3) in Donat-Magnin et al.</p> <p>The following variables are provided:</p> <ul> <li>CC Cloud Cover</li> <li>LHF Latent Heat Flux</li> <li>LWD Long Wave Downward</li> <li>LWU Long Wave Upward</li> <li>QQp Specific Humidity (pressure levels)</li> <li>QQz Specific Humidity (height levels)</li> <li>RH Relative Humidity</li> <li>SHF Sensible Heat Flux</li> <li>SIC Sea ice cover</li> <li>SP Surface Pressure</li> <li>ST Surface Temperature</li> <li>SWD Short Wave Downward</li> <li>SWU Short Wave Upward</li> <li>TI1 Ice/Snow Temperature (snow-layer levels)</li> <li>TTz Temperature (height levels)</li> <li>UUp x-Wind Speed component (pressure levels)</li> <li>UUz x-Wind Speed component (height levels)</li> <li>VVp y-Wind Speed component (pressure levels)</li> <li>VVz y-Wind Speed component (height levels)</li> <li>UVp Horizontal Wind Speed (pressure levels)</li> <li>UVz Horizontal Wind Speed (height levels)</li> <li>ZZp Geopotential Height (pressure levels)</li> <li>mlt Surface melt rate</li> <li>rfz Refreezing rate</li> <li>rnf Rainfall</li> <li>rof "Runoff" (i.e. net production of surface liquid water)</li> <li>sbl Sublimation</li> <li>smb Surface Mass Balance</li> <li>snf Snowfall</li> </ul>
FIGURE 3. Austropolaria magnicirrata n. gen. n in A new genus and species of Polynoidae (Annelida, Polychaeta) from Pine Island Bay, Amundsen Sea, Southern Ocean-a region of high taxonomic novelty
FIGURE 3. Austropolaria magnicirrata n. gen. n. sp., holotype NHM 2012.92 (a) live specimen, dorsal view; (b) preserved specimen, dorsal view; (c) detail of prostomium in dorsal view, style of medina antenna and styles of tentacular cirri missing, damaged; (d) extended pharynx with proboscisidial papillae; (e) pygidial keel, ventral view (Scales: A–C = 1 mm; D–E = 200 µm).
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.